Estrogenic endocrine disruptors (EEDs) are a group of structurally diverse compounds that include pharmaceuticals, dietary supplements, industrial chemicals and environmental contaminants. They can elicit a number of adverse health effects such as hormone dependent cancers, reproductive tract abnormalities, compromised reproductive fitness, and impaired cognitive abilities. In order to fully assess the potential adverse effects of synthetic and natural EEDs, a more comprehensive understanding of their molecular, metabolic, and tissue level effects is required within the context of a whole organism. This collaborative proposal will elucidate the pathways, networks and signaling cascades perturbed by EEDs using the complementary multidisciplinary expertise of its team members in the areas of toxicology, molecular biology, endocrinology, multinuclear NMR spectroscopy, data management and advanced data analysis. The comparative effects of ethynyl estradiol (EE), genistein (GEN), and o, p'-dichlorodiphenyltrichloroethane (DDT) on metabolite levels will be assessed in urine, serum and liver extracts by multinuclear (i. e., 1H, 13C, 31P) NMR spectroscopy, and complemented with histopathology examination and gene expression data from ongoing microarray studies in both mouse and rat models. All data will be stored and archived in dbZach, a MIAME-compliant toxicogenomic supportive database that facilitates data analysis, the integration of disparate data sets, the exchange of data between investigators, and the deposition of data into public repositories. Advanced statistical approaches, modeling and data integration tools such as neural networks, data fusion, and Baysean inference will be used to fuse these disparate data sets in order to elucidate the conserved biological networks that are of importance in response to endogenous estrogens. Moreover, EED perturbed pathways associated with elicited effects will be further defined. Results from these studies will not only further define the physiologic and toxic mechanisms of action of estrogenic compounds but will also demonstrate the synergy of fusing complementary microarray, metabolomic and histopathology data into a comprehensive integrative computational model. This approach will also demonstrate the ability to maximize knowledge extraction from all disparate data available within the proposed innovative data management system when used with the advanced information tools that will be developed.